Interpretable Column Annotation with LLM-Symbolized Decision Process Materialization
Column annotation (CA), including column type annotation (CTA) and column property annotation (CPA), aims to identify the meanings of table columns and the semantic relationships among them. Recent CA methods usually use various neural models to learn column representations and directly map them to label categories, thereby (1) sacrificing model interpretability and adaptivity, and (2) overlooking rich label semantics and ultimately limiting accuracy. To address these limitations, we propose SymCA, an LLM-empowered interpretable CA framework that materializes column annotation as a global-to-local symbolic decision process. SymCA consists of two components: (1) global skeleton induction, ...
| Authors | Mengqi Wang, Jianwei Wang, Qing Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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